Mathematics

Combining data and theory for derivable scientific discovery with AI-Descartes

C. Cornelio, S. Dash, et al.

Dive into groundbreaking research that combines logical reasoning with symbolic regression to uncover the underlying laws of nature! This innovative method effectively reveals governing equations from limited experimental data, as demonstrated through iconic principles such as Kepler's third law and Einstein's time dilation. Conducted by a team of experts including Cristina Cornelio and Sanjeeb Dash, this work promises an exciting leap in scientific modeling.

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Abstract

Scientists aim to discover meaningful formulae accurately describing experimental data. This paper develops a method combining logical reasoning with symbolic regression to derive models of natural phenomena from axiomatic knowledge and data. The method is demonstrated for Kepler's third law, Einstein's time dilation, and Langmuir's adsorption theory, showing the ability to discover governing laws from limited data by using logical reasoning to distinguish between candidate formulae.

Publisher

Nature Communications

Published On

Apr 12, 2023

Authors

Cristina Cornelio, Sanjeeb Dash, Vernon Austel, Tyler R. Josephson, Joao Goncalves, Kenneth L. Clarkson, Nimrod Megiddo, Bachir El Khadir, Lior Horesh

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